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The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.

Forecasting the dynamic line rating allows to reach peaks of operational excellence upon electrical networks. Literature on dynamic rating has mainly addressed overhead lines, whereas lesser attention has been paid to buried cables. However, modeling the dynamics of the cable-soil system is quite a challenge, especially when both the day-ahead and intraday forecasting scenarios have to be considered in order to suit the usual operating tasks on electrical grids. This paper aims at providing a comparison among different forecasting methods, specially developed for such lead times. In particular, this paper: 1) develops a new physical-statistical method for intra-day forecasting scenarios; and 2) verifies the suitability of a data-driven method, which is an adaption of the state-of-the-art approach for dynamic overhead-line rating to the case of dynamic buried-cable rating, for both intraday and day-ahead scenarios. Numerical applications based on actual data are presented to validate the comparative study, and the forecasting results are compared with a naïve benchmark based on the persistence method.

AbstractOver the past years, environmental concerns have played a key role in the development of renewable energy sources (RES). In Europe, the installed capacity of photovoltaic (PV) has increased from around 10 GW in 2008 to nearly 119 GW in 2018 [1]. Due to this high penetration rate and the intermittent nature of RES, several challenges appear related to the economic and secure operation of a power system. To overcome these challenges, it is necessary to develop reliable forecasts of RES, and namely of PV production, for the next hours to days to adjust production planning, while intra-hourly forecasts may contribute to optimize operation of storage units coupled to RES plants.The aim of this paper is to present a novel spatio-temporal (ST) spot forecasting approach able to use multiple heterogeneous sources of data as inputs to forecast short-term PV production (i.e. from 15 minutes up to a day ahead).First, we consider measured production data from nearby power plants as input to forecast the output of a specific PV plant. These data permit to exploit the correlation between the production data of spatially distributed PV sites. The classical ST approach in the literature, based only on this source of data [2], permits to improve predictability for the next few minutes up to 6 hours ahead.Then, we extend the model by the use of satellite images (i.e. global horizontal irradiance (GHI)) which provide meaningful spatial information at a larger extent.Finally, we consider Numerical Weather Predictions (NWPs) as input, which permit to extend the applicability of the model to day-ahead lead times, so that, overall, the resulting model covers efficiently horizons ranging from a few minutes to day ahead.The spatio-temporal relationships being dependent on the particular meteorological situation of the day at hand, we apply an analog ensemble approach, to condition the learning process with historical observations corresponding to similar meteorological situation. We used the analogue approach to select a subset of similar historical situations over which a dynamical calibration of the forecasted model is done, as it was for example suggested by [3,4]. In our paper we extend the analogs ensemble approach by considering geographically distributed observations of the physical variables of interest (as suggested by [4] for hydrological issues) rather than only those at the level of the PV plant.The performance of the proposed ST model with heterogeneous inputs is compared with reference models and advanced ones such as the Random Forest model. Historical production data collected from 9 PV plants of CNR are considered. The power units, located in the South-East France, exhibit relevant spatial correlations which make them suitable for the proposed ST model. References[1] IRENA - https://www.irena.org/Statistics/Download-Data [2] Agoua, Girard, Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy , IEEE, 2018, 9 (2), pp. 538 - 546. https://doi.org/10.1109/TSTE.2017.2747765 [3] Alessandrini, Delle Monache, Sperati, Cervone. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015. https://doi.org/10.1016/j.apenergy.2015.08.011 [4] Bellier, Bontron, Zin. Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resources Research, 2017. https://doi.org/10.1002/2017wr021245

The aggregation of multiple renewable plants located in distinct climate zones, using different energy sources, enables to reduce the production uncertainty when compared to the production of a single plant. Such aggregations, controlled by a Virtual Power Plant (VPP) system, are good candidates for the provision of ancillary services. Stochastic optimization models are available to optimize biddings on ancillary services and energy markets (see for instance [1]). These models require trajectories of the renewable VPP production that anticipate production uncertainty and reproduce correctly the temporal correlations observed in the production signal. This is particularly important in ancillary services markets, where a reserve bid must be guaranteed over a production duration or validity period during which power fluctuations are significant (e.g. lasting currently 24 hours on the European common market for Frequency Containment Reserve, with a foreseen evolution to 4 hours by July 2020 [2]). Production trajectories may be obtained by coupling probabilistic forecasts and a model of temporal dependencies between forecast horizons [3] and possibly spatial dependencies in the case of a multivariate forecast at the scale of a region or a portfolio [4]. In the case of a renewable VPP, the aggregated production is primarily of interest. In this work, we propose a methodology to generate trajectories of aggregated production from probabilistic forecasts obtained with decision-tree based models or neural networks. A copula models the dependency between forecast horizons and the space defined by the plants contained in the aggregation. The model is tested in a day-ahead forecasting configuration on a 54 MW VPP comprising 15 plants with 3 different energy sources (Photovoltaics, Wind, Hydro). The comparison of trajectories generated from a direct forecast of the aggregated production and from forecasts at lower levels of the aggregation shows that the latter solution reproduces with more accuracy the temporal variability of the aggregated production over the whole horizon range, especially when Photovoltaics dominates the production capacities in the aggregation (15 % improvement of the Variogram Score). [1]: Soares, T., & Pinson, P. (2017). Renewable energy sources offering flexibility through electricity markets. Technical University of Denmark.[2]: ENTSO-E. (2018). TSO’s proposal for the establishment of common and harmonised rules and processes for the exchange and procurement of Balancing Capacity for Frequency Containment Reserves (FCR) TSOs’ proposal for the establishment of common and harmonised rules and pro-c, (October), 1–9.[3]: Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B. (2009). From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy, 12(1), 51–62. [4]: Golestaneh, F., Gooi, H. B., & Pinson, P. (2016). Generation and evaluation of space–time trajectories of photovoltaic power. Applied Energy, 176, 80–91.

The decarbonation of electricity production on a global scale is a key element in responding to the pressures of different environmental issues. In addition, the decrease in the costs of the photovoltaic (PV) sector is paving the way for a significant increase in PV production worldwide. The main objective of this thesis is then to maximize the income of a PV energy producer under uncertainty of market prices and production. For this purpose, a probabilistic forecast model of short (5 minutes) and medium (24 hours) term PV production is proposed. This model is coupled with a market participation method that maximizes income expectation. In a second step, the coupling between a PV plant and a battery is studied, and a sensitivity analysis of the results is carried out to study the profitability and sizing of such systems. An alternative participation method is proposed, for which an artificial neural network learns to participate with or without batteries in the electricity market, thus simplifying the process of PV energy valuation by reducing the number of models required.; La décarbonation de la production d’électricité à échelle mondiale est un élément de réponse clé face aux pressions exercées par les différents enjeux environnementaux. Par ailleurs, la baisse des coûts de la filière photovoltaïque (PV) ouvre la voie à une augmentation significative de la production PV dans le monde. L’objectif principal de cette thèse est alors de maximiser le revenu d’un producteur d’énergie PV sous incertitude des prix de marché et de la production. Pour cela, un modèle de prévision probabiliste de la production PV à court (5 minutes) et moyen (24 heures) terme est proposé. Ce modèle est couplé à une méthode de participation au marché maximisant l’espérance du revenu. Dans un second temps, le couplage entre une centrale PV et une batterie est étudié, et une analyse de sensibilité des résultats est réalisée pour étudier la rentabilité et le dimensionnement de tels systèmes. Une méthode de participation alternative est proposée, pour lequel un réseau de neurones artificiel apprend à participer avec ou sans batterie au marché de l’électricité, ce qui permet de simplifier le processus de valorisation de l'énergie PV en diminuant le nombre de modèles requis.

As variable renewable energy plants penetrate significantly the electricity generation mix, they are expected to contribute to the supply of reserve power, albeit the high uncertainty levels on their production. A solution to reduce the uncertainty consists in aggregating renewable plants dispersed over several climates to obtain a smoother production profile and operate them within a Virtual Power Plant control system. In this thesis, a series of probabilistic forecasting models are proposed to assess the capacity of a variable renewable Virtual Power Plant to provide ancillary services with maximum reliability: these models are adapted decision-tree regression models, recurrent and convolutional neural networks, as well as distributions dedicated to extremely low quantiles. The combination of energy sources (Photovoltaics, Wind, Run-of-river Hydro) is considered in detail. Optimal strategies for the joint offer of energy and ancillary services by a variable renewable Virtual Power Plant are later defined, based on production forecasts and market uncertainties. Offer strategies explore several modelling options:dependence between renewable production and prices via a copula, controlled rate of reserve underfullfilment with a chance-constraint optimization, and finally offer of multiple ancillary services thanks to a Lagrangian formulation.; Les énergies renouvelables variables prennent une part croissante de la production raccordée aux réseaux électriques. Par conséquent, elles doivent s’intégrer aux mécanismes de services système qui assurent l’équilibre entre production et con-sommation de puissance sur les réseaux. Toutefois la forte incertitude de la production variable est un obstacle à la fourniture de ces services qui requièrent une fiabilité élevée. L’agrégation de centrales renouvelables dispersées et contrôlées par une centrale virtuelle permet de diminuer cette incertitude en profitant du foisonnement entre les centrales. Cette thèse propose plusieurs modèles de prévision probabiliste afin d’évaluer la capacité d’une centrale virtuelle renouvelable variable à offrir des services système avec une fiabilité maximale: ces modèles sont des adaptations d’arbres de décisions, de réseaux de neurones récurrents et convolutifs, ainsi que de distributions dédiées aux quantiles extrêmement faibles. Une attention particulière est portée à la combinaison de sources d’énergie (Photovoltaïque, éolien, hydraulique au fil de l’eau). Ensuite, des stratégies d’offre optimale d’énergie et de réserve par une agrégation renouvelable sont établies en utilisant les prévisions de production et en considérant les incertitudes associées aux différents marchés. Ces stratégies explorent plusieurs options de modélisation: dépendance entre production renouvelable et prix par une copule, taux de défaillance contrôlé par optimisation sous contraintes probabilistes, et enfin offre de services système multiples à l’aide d’une formulation Lagrangienne.

As variable renewable energy plants penetrate significantly the electricity generation mix, they are expected to contribute to the supply of reserve power, albeit the high uncertainty levels on their production. A solution to reduce the uncertainty consists in aggregating renewable plants dispersed over several climates to obtain a smoother production profile and operate them within a Virtual Power Plant control system. In this thesis, a series of probabilistic forecasting models are proposed to assess the capacity of a variable renewable Virtual Power Plant to provide ancillary services with maximum reliability: these models are adapted decision-tree regression models, recurrent and convolutional neural networks, as well as distributions dedicated to extremely low quantiles. The combination of energy sources (Photovoltaics, Wind, Run-of-river Hydro) is considered in detail. Optimal strategies for the joint offer of energy and ancillary services by a variable renewable Virtual Power Plant are later defined, based on production forecasts and market uncertainties. Offer strategies explore several modelling options:dependence between renewable production and prices via a copula, controlled rate of reserve underfullfilment with a chance-constraint optimization, and finally offer of multiple ancillary services thanks to a Lagrangian formulation.; Les énergies renouvelables variables prennent une part croissante de la production raccordée aux réseaux électriques. Par conséquent, elles doivent s’intégrer aux mécanismes de services système qui assurent l’équilibre entre production et con-sommation de puissance sur les réseaux. Toutefois la forte incertitude de la production variable est un obstacle à la fourniture de ces services qui requièrent une fiabilité élevée. L’agrégation de centrales renouvelables dispersées et contrôlées par une centrale virtuelle permet de diminuer cette incertitude en profitant du foisonnement entre les centrales. Cette thèse propose plusieurs modèles de prévision probabiliste afin d’évaluer la capacité d’une centrale virtuelle renouvelable variable à offrir des services système avec une fiabilité maximale: ces modèles sont des adaptations d’arbres de décisions, de réseaux de neurones récurrents et convolutifs, ainsi que de distributions dédiées aux quantiles extrêmement faibles. Une attention particulière est portée à la combinaison de sources d’énergie (Photovoltaïque, éolien, hydraulique au fil de l’eau). Ensuite, des stratégies d’offre optimale d’énergie et de réserve par une agrégation renouvelable sont établies en utilisant les prévisions de production et en considérant les incertitudes associées aux différents marchés. Ces stratégies explorent plusieurs options de modélisation: dépendance entre production renouvelable et prix par une copule, taux de défaillance contrôlé par optimisation sous contraintes probabilistes, et enfin offre de services système multiples à l’aide d’une formulation Lagrangienne.

A plasma-catalytic reactor consisting in a vertical coaxial dielectric barrier discharge reactor filled with solid catalysts was developed to directly synthesize liquid chemicals (alcohols, acids, hydrocarbons) and syngas from CO2 and CH4 at atmospheric conditions. Co and Fe catalysts prepared via incipient wetness impregnation method were loaded on a SiO2 aerogel support synthesized by ambient drying method after surface modification. Discharges were generated in different ratios of CO2/CH4 mixtures at ambient conditions and the performance of the catalysts was evaluated. In this study, the total liquid selectivity was approximately 40% after introducing the catalysts, where main liquid products were methanol and acetic acid. By varying the CH4 and CO2 ratio, a small number of long-chain hydrocarbons and alcohols were also detected with our catalysts. The synergy of plasma-catalysis in this process demonstrates great potential for the direct synthesis of value-added liquid chemicals and fuels from CO2 and CH4.

International audience; In the prospect of a large scale deployment of Renewable Energy for electricity production, plasmas will definitively be a major option to get tuneable, high temperature enthalpy sources without direct CO2 emissions. This paper focuses on the direct decomposition of methane for the simultaneous synthesis of hydrogen and high value-added carbon black. After a review of gas phase carbon particle nucleation and growth physico-chemical phenomena, a new original model for the plasma decomposition of methane is presented. The model solves a reactive turbulent flow in a 3D geometry. The nucleation is based on a detailed reaction mechanism and the particle growth is handled by a sectional method. This model opens the way towards a better understanding of carbon particles gas phase nucleation and growth and consequently to a fine control of high value-added carbon black grades.

International audience; In the last decades, renewable energy sources have been increasing their shares in the world energy market. In addition to the ecological benefits, this trend can have adjunct benefits, for example for distribution system operators: a gain in their grid sizing. Indeed, installation of decentralized production, when used in a self-consumption approach, can lead to reduction of the consumption peaks. This work is willing to quantify what grid sizing reduction a distribution system operator can expect, knowing the renewable energies penetration rate on a MV feeder. To do so, a description of the actual sizing strategy is first described. Estimation of electricity demand is performed using a bottom-up simulation method while photovoltaics and wind power production are evaluated with reanalysis data coupled with a new method to inject variability to the smooth curves. This procedure leads to a new sizing power which can be used, guaranteeing an equivalent quality of supply for consumers. For the tested MV feeders, a maximum reduction of about 4 % of the sizing power is observed. Lastly, an analysis of the under-sizing risk is carried out, characterizing the error in the new sizing power estimation with the number of scenarios taken into account.

International audience; New hybrid organic-inorganic benzoxazine aerogel blankets for space applications have been synthesized and studied. Aerogel blankets were produced using a one-pot synthesis method with a PET unwoven fibrous network core, resorcinol, formaldehyde and a silica source (APTES with MTES or MTMS as silica co-precursors). Modifying the composition of the sol significantly impacts the physical characteristics of the resulting material such as texture (density and porosity), hydrophilicity, and thermal conductivity. The apparent density of the materials decreases when the percentage of solid in the sol (%solid) decreases and the molar ratio nMTES/nAPTES increases. Within the density range studied, apparent density and effective thermal conductivities are inversely proportional. By replacing a part of APTES by MTES or MTMS the density of the aerogels decreases notably whilst maintaining the level of thermal conductivity. This replacement is detrimental to water adsorption which increases for most conditions studied. When MTMS is used as silica co-precursor instead of MTES no significant differences in water adsorption and thermal conductivity were found. Using the RF-APTES-MTES chemical system we were able to obtain a 3%solid flexible hybrid blanket with both low apparent density (0.04 g cm−3) and relatively low effective thermal conductivity (0.027 W m−1 K−1 in room conditions). A proof of concept with large panels of this material was done by evaluating them as prototype materials composed of encapsulated aerogel blanket in vacuum and low pressure atmosphere. The blankets show a combination of key characteristics representing the right mix for thermal insulation in space applications operating in non-vacuum environments.

International audience; In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models. When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed. We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties. We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.

International audience; The use of high amounts of iridium in industrial proton exchange membrane water electrolyzers (PEMWE) could hinder their widespread use for the decarbonization of society with hydrogen. Nonthermally oxidized Ir nanoparticles supported on antimony-doped tin oxide (SnO2:Sb, ATO) aerogel allow decreasing the use of the precious metal by more than 70% while enhancing the electrocatalytic activity and stability. To date, the origin of these benefits remains unknown. Here, we present clear evidence of the mechanisms that lead to the enhancement of the electrochemical properties of the catalyst. Operando near-ambient pressure X-ray photoelectron spectroscopy on membrane electrode assemblies reveals a low degree of Ir oxidation, attributed to the oxygen spill-over from Ir to SnO2:Sb. Furthermore, the formation of highly unstable Ir(III) species is mitigated, while the decrease of Ir dissolution in Ir/SnO2:Sb is confirmed by inductively coupled plasma mass spectrometry. The mechanisms that lead to the high activity and stability of Ir catalysts supported on SnO2:Sb aerogel for PEMWE are thus unveiled.

The French residential and tertiary sectors account for about 45% of energy consumption and 20% of greenhouse gas emissions, constituting a large savings potential which is relatively easy to mobilise in comparison with other sectors such as transport or industry. This thesis develops a decision-aiding method for territories stakeholders willing to implement building energy retrofit measures. This method relies on a building stock modeling based on the enrichment of heterogeneous databases available to local decision-makers. An optimization problem of energy renovation measures activates levers at the building-level over the territory, turning dimensionality into a key issue this work. A meta-model of building heating energy consumption, adapted to this context, is constructed from a dynamic urban model. A dedicated characterisation of both thermal retrofit and energy systems is conducted, basing solutions on observations of the current French building context. Both modeling and optimization methods are finally applied to real territories, and used to conduct studies related to the energy transition context., Les secteurs résidentiel et tertiaire français représentent environ 45% de la consommation d'énergie et 20% des émissions de gaz à effet de serre, constituant un gisement d'économies important et relativement facilement mobilisable en comparaison à d'autres secteurs comme le transport ou l'industrie. Cette thèse développe une méthode d'aide à la décision territoriale pour la mise en oeuvre opérationnelle de la rénovation énergétique des bâtiments. Cette méthode repose sur une caractérisation des territoires basée sur l'enrichissement de bases de données hétérogènes et disponibles aux acteurs locaux. Un problème d'optimisation des mesures de rénovation énergétique permet d'actionner des leviers au niveau de chaque bâtiment du territoire, mettant la question de la dimensionnalité au cœur de ce travail. Un méta-modèle de la consommation énergétique de chauffage des bâtiments, adapté à ce contexte, est construit à partir d'un modèle dynamique urbain. Les solutions techniques de rénovation modélisées reposent sur une observation du contexte actuel français, et font d'objet d'une caractérisation technico-économique dédiée. Les méthodes de modélisation et d'optimisation sont enfin appliquées à différents territoires, et exploitées pour mener des études en lien avec le contexte de la transition énergétique.

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This thesis deals with the study of Fischer-Tropsch synthesis (FTS) and the direct conversion of CO2 and CH4 into value-added liquid products by a process coupling non-thermal plasma and heterogeneous catalysis. A coaxial dielectric barrier discharge (DBD) plasma-catalytic reactor was designed and tested in this thesis with its analysis and diagnostic devices. The reactor operates under ambient conditions, the catalyst and its support are under the form of granules packaged in the reactor. The support is in silica aerogel and was synthesized by surface treatment and ambient drying. Chapter 3 is dedicated to the experimental study of plasma-catalytic Fischer-Tropsch synthesis. In this chapter, the experiments were first conducted in plasma alone without catalyst, paying particular attention to the influence of the gas flow rate, the H2/CO ratio and the electrical excitation frequency on the syngas conversion. Then, in a second step, the catalyst/support couple (Co/SiO2) was introduced into the reactor in order to study the results of the conversion under plasma-catalytic combination. Chapters 4 and 5 deal with the direct plasma-catalytic conversion of CO2 and CH4 into liquid products and syngas. In Chapter 4, the same approach as in Chapter 3 was used, a comparative study of experiments with and without catalyst was done. The two catalyst/support couples used successively in this chapter are Co/SiO2 and Fe/SiO2. Chapter 5 extends the study by proposing a plasma-catalytic combination with two catalysts at the same time. The effects of packing composite catalysts (Co/SiO2 or Fe/SiO2 with HZSM-5, Co/HZMS-5 and Fe/HZSM-5) were studied. The combination of catalysts has made it possible to increase the liquid products selectivity and to produce new liquid products compared to Chapter 4. Thus alcohols, carboxylic acids and C5+ hydrocarbons were synthesized. The thesis ends with a comparison of the results with those of the literature from close processes; energy efficiency and gross productivity were compared. Some possible ways of reaction have also been proposed to initiate a more theoretical reflection. Finally, recommendations for optimization/improvement of FTS and direct conversion of CO2/CH by plasma-catalytic route are provided and discussed.; Cette thèse porte sur l’étude de la synthèse Fischer-Tropsch (FTS) et de la conversion directe de CO2 et de CH4 en produits liquides à valeur ajoutée par un procédé couplant plasma non-thermique et catalyse hétérogène. Un réacteur plasmocatalytique de type DBD coaxial rempli a été conçu et testé dans cette thèse avec ses appareils d'analyse et de diagnostics. Le réacteur fonctionne dans des conditions ambiantes, le catalyseur et son support se présentent sous la forme de granules empaquetées dans le réacteur. Le support est en aérogel de silice et a été synthétisé par traitement de surface puis séchage ambient. Le chapitre 3 est dédié à l’étude expérimentale de la synthèse Fischer-Tropsch plasmocatalytique. Dans ce chapitre, les expériences ont d’abord été conduites en plasma seul sans catalyseur en portant une attention particulière à l’influence du débit de gaz, du ratio H2/CO et de la fréquence d’excitation électrique sur la conversion du gaz de synthèse. Puis, dans un deuxième temps, le couple catalyseur/support (Co/SiO2) a été introduit dans le réacteur afin d’étudier les résultats de la conversion en association plasmocatalytique. Les chapitres 4 et 5 portent eux sur la conversion plasmocatalytique directe du CO2 et du CH4 en produits liquides et gaz de synthèse. Dans le chapitre 4, la même démarche que celle du chapitre 3 a été employée, à savoir la conduite comparative d’expériences avec et sans catalyseur. Les couples deux catalyseur/support utilisés successivement dans ce chapitre sont Co/SiO2 et Fe/SiO2. Le chapitre 5 étend l’étude en proposant une association plasmocatalytique à deux catalyseurs. Ainsi des combinaisons binaires parmi les couples suivants Co/SiO2, Fe/SiO2, HZSM-5/HZSM-5, Co/HZMS-5 et Fe/HZSM-5 ont été étudiées. Ce couplage a permis d’augmenter la sélectivité vis-à-vis des produits liquides produits en chapitre 4 et d’en produire de nouveaux. Ainsi des alcools, acides carboxyliques et des hydrocarbures C5+ ont été synthétisés. La thèse finit par une comparaison des résultats avec ceux de la littérature provenant de procédés proches ; l’efficacité énergétique et la productivité brute ont notamment été comparées. Quelques voies de réaction possibles ont également été proposées pour initier une réflexion plus théorique. Enfin, des recommandations en vue d’une optimisation/amélioration de la FTS et de la conversion directe CO2/CH4 par voie plasmocatalytique sont fournies et discutées.

Distributed energy resources (DERs) installed in active distribution networks (ADNs) can be exploited to provide both active and reactive power reserves to the upper-layer grid (i.e., sub-transmission and transmission systems) at their connection point. This paper introduces a method to determine the capability area of an ADN for the provision of both active and reactive power reserves while considering the forecast errors of loads and stochastic generation, as well as the operational constraints of the grid and DERs. The method leverages a linearized load flow model and introduces a set of linear scenario-based robust optimization problems to estimate the reserve provision capability (RPC) area of the ADN. It is proved that, under certain assumptions, the RPC area is convex. The performance of the proposed method is tested on a modified version of the IEEE 33-bus distribution test system.

This thesis deals with the study of Fischer-Tropsch synthesis (FTS) and the direct conversion of CO2 and CH4 into value-added liquid products by a process coupling non-thermal plasma and heterogeneous catalysis. A coaxial dielectric barrier discharge (DBD) plasma-catalytic reactor was designed and tested in this thesis with its analysis and diagnostic devices. The reactor operates under ambient conditions, the catalyst and its support are under the form of granules packaged in the reactor. The support is in silica aerogel and was synthesized by surface treatment and ambient drying. Chapter 3 is dedicated to the experimental study of plasma-catalytic Fischer-Tropsch synthesis. In this chapter, the experiments were first conducted in plasma alone without catalyst, paying particular attention to the influence of the gas flow rate, the H2/CO ratio and the electrical excitation frequency on the syngas conversion. Then, in a second step, the catalyst/support couple (Co/SiO2) was introduced into the reactor in order to study the results of the conversion under plasma-catalytic combination. Chapters 4 and 5 deal with the direct plasma-catalytic conversion of CO2 and CH4 into liquid products and syngas. In Chapter 4, the same approach as in Chapter 3 was used, a comparative study of experiments with and without catalyst was done. The two catalyst/support couples used successively in this chapter are Co/SiO2 and Fe/SiO2. Chapter 5 extends the study by proposing a plasma-catalytic combination with two catalysts at the same time. The effects of packing composite catalysts (Co/SiO2 or Fe/SiO2 with HZSM-5, Co/HZMS-5 and Fe/HZSM-5) were studied. The combination of catalysts has made it possible to increase the liquid products selectivity and to produce new liquid products compared to Chapter 4. Thus alcohols, carboxylic acids and C5+ hydrocarbons were synthesized. The thesis ends with a comparison of the results with those of the literature from close processes; energy efficiency and gross productivity were compared. Some possible ways of reaction have also been proposed to initiate a more theoretical reflection. Finally, recommendations for optimization/improvement of FTS and direct conversion of CO2/CH by plasma-catalytic route are provided and discussed.; Cette thèse porte sur l’étude de la synthèse Fischer-Tropsch (FTS) et de la conversion directe de CO2 et de CH4 en produits liquides à valeur ajoutée par un procédé couplant plasma non-thermique et catalyse hétérogène. Un réacteur plasmocatalytique de type DBD coaxial rempli a été conçu et testé dans cette thèse avec ses appareils d'analyse et de diagnostics. Le réacteur fonctionne dans des conditions ambiantes, le catalyseur et son support se présentent sous la forme de granules empaquetées dans le réacteur. Le support est en aérogel de silice et a été synthétisé par traitement de surface puis séchage ambient. Le chapitre 3 est dédié à l’étude expérimentale de la synthèse Fischer-Tropsch plasmocatalytique. Dans ce chapitre, les expériences ont d’abord été conduites en plasma seul sans catalyseur en portant une attention particulière à l’influence du débit de gaz, du ratio H2/CO et de la fréquence d’excitation électrique sur la conversion du gaz de synthèse. Puis, dans un deuxième temps, le couple catalyseur/support (Co/SiO2) a été introduit dans le réacteur afin d’étudier les résultats de la conversion en association plasmocatalytique. Les chapitres 4 et 5 portent eux sur la conversion plasmocatalytique directe du CO2 et du CH4 en produits liquides et gaz de synthèse. Dans le chapitre 4, la même démarche que celle du chapitre 3 a été employée, à savoir la conduite comparative d’expériences avec et sans catalyseur. Les couples deux catalyseur/support utilisés successivement dans ce chapitre sont Co/SiO2 et Fe/SiO2. Le chapitre 5 étend l’étude en proposant une association plasmocatalytique à deux catalyseurs. Ainsi des combinaisons binaires parmi les couples suivants Co/SiO2, Fe/SiO2, HZSM-5/HZSM-5, Co/HZMS-5 et Fe/HZSM-5 ont été étudiées. Ce couplage a permis d’augmenter la sélectivité vis-à-vis des produits liquides produits en chapitre 4 et d’en produire de nouveaux. Ainsi des alcools, acides carboxyliques et des hydrocarbures C5+ ont été synthétisés. La thèse finit par une comparaison des résultats avec ceux de la littérature provenant de procédés proches ; l’efficacité énergétique et la productivité brute ont notamment été comparées. Quelques voies de réaction possibles ont également été proposées pour initier une réflexion plus théorique. Enfin, des recommandations en vue d’une optimisation/amélioration de la FTS et de la conversion directe CO2/CH4 par voie plasmocatalytique sont fournies et discutées.

International audience

International audience; Uncertainty in the upcoming production of photo-voltaic (PV) plants is a challenge for grid operations and also a source of revenue loss for PV plant operators participating in electricity markets, since they have to pay penalties for the mismatch between contracted and actual productions. Improving PV predictability is an area of intense research. In real-world applications, forecasts are often needed for different time frames (horizon, update frequency, etc.) and are derived by dedicated models for each time frame (i.e. for day ahead and for intra-day trading). This can result in both different forecasted values corresponding to the same horizon and discontinuities among time-frames. In this paper we address this problem by proposing a novel seamless probabilistic forecasting approach able to cover multiple time frames. It is based on the Analog Ensemble (AnEn) model, however it is adapted to consider the most appropriate input for each horizon from a pool of available input data. It is designed to be able to start at any time of day, for any forecast horizon, making it well-suited for applications like continuous trading. It is easy to maintain as it adapts to the latest data and does not need regular retraining. We enhance short-term predictability by considering data from satellite images and in situ measurements. The proposed model has low complexity compared to benchmark models and is trivially parallelizable. It achieves performance comparable to state-of-the-art models developed specifically for the short term (i.e. up to 6 hours) and the day ahead. The evaluation was carried out on a real-world case comprising three PV plants in France, over a period of one year.

This paper is dedicated to the experimental study of FTS synthesis via a non-thermal plasma-catalytic pathway under ambient conditions. In our study, a vertical tubular DBD reactor containing packed catalysts was established with its analytical apparatus. Different Co/SiO2 aerogel catalysts were synthesized by ambient drying method and then characterized. The variable studies were conducted without packing, and then the performance of the catalysts was evaluated. The results indicate that a considerable concentration of C1 to C5 hydrocarbons were synthesized at ambient conditions with relatively low specific input energies (below 18 kJ/L). Moreover, the Co/SiO2 aerogel catalysts can promote not only the synthesis of C2-C5 hydrocarbons rather than CH4 and CO2 but also the formation of liquid organic oxides. The possible reaction pathways of the non-thermal plasma promoted FTS associated with catalysis were proposed. The work demonstrates a new approach of FTS and broadens the field of further improvement on FTS.

International audience; Demand-side management (DSM) represents a potential way to improve the profitability of renewable energy systems. In thispaper, power management including a new DSM strategy in a stand-alone hybrid Photovoltaic (PV)/ Diesel/ Battery system with multiplecustomers has been studied. A new probabilistic model of the consumer behavior based on Bayesian network and Monte Carlo simulation hasbeen carried out so as to capture the real-time and stochastic aspect of the demand. The analysis has been made by means of a one-year periodsimulation of the whole system. Statistical data on consumers and meteorological observation data have been used to set the simulation’sparameters. Numerical results showed that with the implementation of DSM, energy costs are reduced by 11.3% for equal total consumption, andthe use of solar energy resources rose to 54%. This provides insight on the significant performance enhancement offered by a DSM scheme insuch a system.

International audience

International audience; The general public expects relevant, comprehensible and acceptable communication on climate change. Many efforts have been, and are still, being made to make the message clear and comprehensible. This paper focuses on the acceptability of the message by the receiver, to move from awareness of climate change to concrete action, which is seldom discussed in the literature. In order to make a climate change communication assessment, we choose to take as reference the Prochaska’s behavioural stage of change model. Our analysis suggests that taking into account emotions and cognition mechanisms is needed in order to accompany people to better process the information and integrate it to move toward action. This paper highlights different commonly used communication practices and the underlying brain mechanisms involved in each one. A better understanding of those mechanisms should help to improve the message’s receivability in communication about climate change. In turn, it will help to move from individuals knowledge into concrete action. By this way, we hope to provide inspiration to communicators in order to better accompany people in their process from awareness to action.

International audience; In an increasingly decentralised energy system with a rising share of fluctuating renewable energy sources, such as wind energy and photovoltaics (PV), the importance of virtual power plants (VPP) for the provision of ancillary services is growing. Transmission system operators (TSOs) impose stringent requirements on the reliability and accurate controllability of power plants that are susceptible to qualify for control reserve provision. It needs to be discussed whether these requirements have to evolve to enable the participation of renewables in control reserve markets. As part of the REstable research project, an innovative ICT infrastructure was set up for linking German and French wind and PV farms in a transnational VPP. Key performance indicators (KPIs) derived from technical requirements of the German TSOs are proposed and applied to quantify the quality of control reserve provision by the VPP in physical field tests

Isbn : 978-1-5386-8218-0/19/; International audience; Electrical demand in the world especially residential demand is increasing. Instead of building new conventional power plants, distributed generation based on renewable resources are an alternative solution to cope the demand. Simultaneously, government of some countries release their regulation to encourage households be active player in the market. However, in several years now, Feed in Tariff (FiT) is decreased by the regulation. Demand side management strategies especially load shifting are used to reduce the electric cost and trying to handle household satisfaction. To reach the similar goal in the community, coordination of energy management is necessary where customers maximize their self-consumption through PV sharing. Using energy contract and Particle Swarm Optimization it is expected to help the costumers to find their optimal cost, and to reach a maximum self-consumption rate.

International audience; Fe-N-C catalysts have been regarded as the most promising alternative of non-platinium-group-metal (non-PGM) catalysts towards oxygen reduction reaction(ORR) in PEM fuel cells, due to the low price and significant breakthrough in ORR activity and durability in recent decades [1][2]. Numerous studies have been conducted to make further advances in their performance and gain the insight into their catalytic mechanism to ORR, by using different synthesis methods with various carbon, nitrogen and Iron precursors along with a series of optimization of synthesis conditions (e.g. temperature and nature of gases of heat treatment). Nevertheless, some mass transport issues remain. Herein, we developed a highly effective, 3D Fe-N-C aerogel catalysts with resorcinol (R)-formaldehyde (F)-melamine(M) and Iron precursor using “one pot “sol-gel process followed by supercritical drying and heat-treatment in N2and then ammonia (NH3) atmosphere(Figure 1 left). The optimization of Fe-N-C aerogel catalysts was undertaken by varying the initial sol pH,solid (reactants R+M+F) content in the initial solution and heat-treatment parameters. The physico-chemical properties of catalysts were characterized by XRD, SEM, XPS and N2-adsorption/desorption, and the electro-catalytic performance was studied by using cyclic voltammetry and linear sweep voltammetry (rotating disk electrode method) in 0.05 M H2SO4aqueous solution.The correlation between physicochemical characterizationsand the electrochemical performancesof the Fe-N-C aerogel catalysts were discussed, established and compared to Pajarito catalyst.The optimized catalysts with high specific surface area of 1652 m2/gand high content of pyridineN, exhibits an outstandingORR performancesand a better mass transport property compared to the Pajarito catalyst.

International audience; The objective of this work is to reduce the storage dimensions required to operate a coupled photovoltaic (PV) and Battery Energy Storage System (BESS) in an electricity market, while keeping the same level of performance. Performance is measured either with the amount of errors between the energy sold on the market and the actual generation of the PV/BESS i.e. the imbalance, or directly with the revenue generated on the electricity market from the PV/BESS operation. Two solutions are proposed and tested to reduce the BESS size requirement. The first solution is to participate in electricity markets with an aggregation of several plants instead of a single plant, which effectively reduces the uncertainty of the PV power generation. The second is to participate in an intra-day market to reduce the BESS usage. To evaluate the effects of these two solutions on the BESS size requirement, we simulate the control of the PV/BESS system in an electricity market.

Open access article available at : https://iopscience.iop.org/article/10.1088/1742-6596/1343/1/012029/meta; International audience; Reduction of the energy consumption is a key lever to tackle climate change, but identification of the retrofit actions to undertake within a building stock remains a challenging scientific problem. This paper presents a complete methodology able to design action plans at a territory level with a building resolution. The economic and energetic modeling of the retrofit context is detailed before introducing a linear 0-1 optimization formulation which is used to arbitrate between both building envelope insulation and heating system replacement measures. A 500 buildings territory study case is then presented to illustrate the potential of the developed tool.

International audience; Anticipating electricity prices on the day-ahead market has become a key issue for both risk assessment and revenue optimization. In this paper, we propose to generate time series of prices with an hourly resolution using a structural model that simulates a simplified market clearing process. The aggregated supply curves in this model are composed of orders based on the available capacity of generation units. The ask prices are parametrized, and the calibration is performed by applying statistical learning to historical market and power system data. To reflect the strategic behavior of market participants, these prices depend on the scarcity of power at the national level. The model's performance is assessed based on the case of France with a one-year horizon and data from 2013-2015. This approach illustrates how open data on the electric power system enable links to be drawn between technical constraints and price formation.

International audience

International audience; Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Task on Wind Power Forecasting organises international collaboration, among national weather centres, forecast vendors and forecast users. The Task looks back on the first 3 years, and just started the second three-year period. Collaboration is open to IEA Wind member states, 13 countries are already therein.The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets and a benchmark. Secondly, we try to improve the derived power forecasts and deal with forecast vendor related matters to bring the entire industry forward. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.The main result of the first phase is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, Designing and executing forecasting benchmarks and trials and Benchmark metrics) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls we have seen over the years, are avoided. Other results include a comprehensive review paper on the use of uncertainty forecasts in the power industry and an information portal related to forecasting.In short, the poster presents the IEA Task 36 on Wind Power Forecasting, opening a forum for international collaboration in this important field for meteorologists, wind power forecasters and end users. For collaboration, please contact the author (grgi@dtu.dk) and see the website at www.ieawindforecasting.dk.

International audience; The valorization of photovoltaic (PV) energy generation involves several decision making processes at different times with different objectives. For example, a PV power plant coupled with a Battery Energy Storage System (BESS) has to provide bids in the day-ahead electricity market, but can also provide ancillary services. On the delivery day, it can also participate in intra-day trading sessions, and must decide which quantity to charge or discharge from the BESS in real-time. These successive decision-making processes all require forecasts of the energy production level for different forecast horizons.However, the models and the inputs used for the different forecast horizons are often different. A common result is that in situ measurements are more accurate for very-short term forecasts (real-time to one hour ahead forecasts), satellite data is better for short-term forecasts (up to 6 hours ahead), and Numerical Weather Predictions (NWP) are better for long-term forecasts (day-ahead and longer). Models also vary, with auto-regressive approaches being commonly used for very-short term forecasts, while longer forecast horizons use a wide range of machine learning models.The RES producers have thus to develop and maintain numerous forecasting models for the different decision-making processes they are involved in, usually fitted for each power plant. This increases further the complexity of the decision-making processes and can create problems regarding the continuity of the forecasts.In this work we propose a forecasting model for PV power generation that can use all the inputs mentioned before, and weights them according to the forecasting horizon. It can thus operate from very short-term to day-ahead forecast horizons with state-of-the-art performance. It can also directly provide probabilistic forecasts for an aggregation of power plants, thus allowing having a single forecasting model for managing a virtual power plant. The model follows the “lazy learning” paradigm, where generalization from the training set is only computed when a forecast is requested. Thus, the model is resilient to changes in the neighborhood of the plant (surrounding environment, partial outage, soiling, etc.)The model is based on the Analog Ensemble (AnEn) method. However it is structurally expanded to allow the method to use an arbitrary large number of inputs. Each input is then weighted depending on the forecast horizon. As an example, for a given input for one-hour ahead, the weight is computed based on the Mutual Information (MI) between the input and the PV power generation observed one hour later. This allows dynamically selecting the most relevant inputs depending on the horizon. The model is evaluated for short-term and day-ahead forecasts, and compared with a Quantile Regression Forest (QRF) for day-ahead forecasts, and a linear Auto-Regressive Integrated Moving Average (ARIMA) model for the short term forecasts. Results show that the AnEn model is competitive with the QRF model in day-ahead forecasting. It is also consistently better than the ARIMA model for short-term forecasting.

International audience; The flexibility of distributed energy resources (DERs) accommodated in active distribution networks (ADNs) can be aggregated and then used to provide ancillary services to the transmission system. In this context, this paper presents a linear optimization method for the transmission system operator (TSO) to allocate its required active power reserve from aggregated resources installed in active distribution systems (ARADSs) as well as dispatchable bulk power plants (DBPPs). It consists in a linear optimization problem that minimizes the sum of the expected cost of active power reserve allocated from all possible providers (including ARADSs and DBPPs) and the expected cost of load not served over a desired time horizon. The value of lost load (VOLL) index is used as a criterion to realize an economical balance between the expected cost of allocated reserve and expected cost of load not served. The method leverages scenarios of power system contingencies and forecast errors of loads and renewable generation to represent typical operational uncertainties. A simulation proof-of-concept using real-data from the transmission system operator of Switzerland, Swissgrid, is provided to illustrate the performance of the method. Index Terms-Active distribution network (ADN), active power reserve, aggregated resources of active distribution system (ARADS), energy storage system (ESS), distributed energy resource (DER).

International audience; The paper presents the results of a study on replicability and scalability carried out within the SENSIBLE project. In this study a simplified methodology for simulating the behaviour of a market based distributed energy storages coordination has been implemented. Attention has been paid to capture the core of the problems without falling into excessive details of the different solutions (centralised, decentralised) developed. After this, data have been gathered or simulated for a large number of necessary datasets, including electricity consumption in different neighbours, electricity wholesale and retail prices, in six European countries. Central to this stage has been the use of BUS (Bottom Up Simulator) a load profiles simulator developed at MINES ParisTech which has been used to simulate load curves in the different European countries considering historical local weather conditions. Index Terms-Energy storage, Batteries, Power system management I. INTRODUCTION Scalability and replicability are relevant concepts for the evaluation of the benefits of innovative solutions. The attention on these concepts is especially high in R&D projects, where solutions are developed and tested in limited demonstrators. In order to understand the potential of the solutions tested, an evaluation of their performance in other conditions is therefore necessary, but extremely difficult due to the complex nature of the problem. According to [1] both problems can be seen as the result of the interaction of four quite diverse factors: 1) technical, 2) economic, 3) regulatory and 4) acceptance. For this reason evaluations of replicability and scalability have been conducted though surveys or discussions with experts such as in [1], [2] and [3].

This thesis introduces novel electrolyte membranes which can be operated at low relative humidity (below 50%) and intermediate temperature, i.e., 90℃. More specifically, the thesis takes benefit from hygroscopicity of microfibrous SEP (sepiolite) and tubular HNT (halloysite). Changes in Nafion membrane properties with blending time were studied. Moreover, these nanoclays are functionalized and pretreated to make them proton conductive and to improve their compatibility with short-side-chain PFSA (perfluorosulfonic acid) composite membranes based on Aquivion. To begin with, functionalized and pretreated clay nanoparticles are characterized prior to incorporation in polymer matrix: ATR-FTIR (attenuated total reflection-fourier transform infrared spectroscopy), Py-GC/MS (pyrolysis gas chromatography mass spectrometry), and TGA (thermogravimetric analysis). Composites membranes have them been prepared and characterized for proton conductivity, water uptake, swelling, thermo-mechanical strength and chemical stability. The dispersion state of SEP and HNT inside polymer phase was observed using SEM/EDS (field emission scanning electron microscopy/Energy dispersive X-ray spectroscopy). The properties of pretreated nanoclays are characterized using XRD (X-ray diffraction) and EDS. Chemical stability regarding radical attack against composite membranes is clarified using Ion meter through fluoride ion (F-) analysis. Proton conductivity of composite membranes is also measured under condition of different relative humidity and temperature. Following this, it is demonstrated by DMA (dynamic mechanical analysis) results that the particular elongated morphology of SEPs and HNTs participates to improving mechanical property of the composite membranes with decreased swelling ratio. MEAs (membrane electrode assembly) performance are evaluated to understand the advantage of the presence of nanoclays in the composite membranes regarding the relative humidity of the feeding gas, the operating temperature of the cell, and the hydrogen crossover. Detailed abstracts including main results were provided at the beginning of each chapter., Cette thèse introduit de nouvelles membranes électrolytiques pouvant fonctionner à faible humidité relative (inférieure à 50%) et à une température intermédiaire, c'est-à-dire 90°C voire au-delà. Plus spécifiquement, la thèse tire profit de l'hygroscopicité de la morphologie d’argiles naturelles, lasépiolite microfibreuse et l’halloysite tubulaire . Ces nanoargiles ont été intégrées à des suspensions de Nafion® ou Aquivion pour préparer des membranes composites. Elles ont été fonctionnalisées et prétraitées pour les rendre conductrices protoniques et améliorer leur compatibilité avec les matrices perfluorosulfoniques utilisés. Ces argiles ont d’abord été caractérisées avant leur incorporation dans la matrice polymère : ATR-FTIR (spectroscopie infrarouge à transformée de Fourier totale atténuée), Py-GC/MS (spectrométrie de masse par chromatographie en phase gazeuse à pyrolyse) et ATG (analyse thermogravimétrique). Les propriétés des nanoargiles prétraitées ont enfin été caractérisées par XRD (diffraction des rayons X) et EDS. Les membranes composites préparées ont ensuite été caractérisées pour la conductivité protonique, l'absorption d'eau, le gonflement, la résistance thermomécanique et la stabilité chimique. L'état de dispersion des argiles à l'intérieur de la phase de polymère a été observé par SEM/EDS (microscopie électronique à balayage à émission de champ / spectroscopie à rayons X à dispersion d'énergie). La stabilité chimique vis-à-vis de l'attaque radicale contre les membranes composites a été étudiée par mesure de la formation d’ions fluorure (F-). La conductivité protonique des membranes composites a également été calculée à partir des résistances mesurées dans dans une large gamme d'humidités relatives et de températures. Des mesures thermomécaniques par analyse mécanique dynamique ont montré que la morphologie allongée particulière des argiles choisies participe à l'amélioration des propriétés mécaniques des membranes composites tout en réduisant le taux de gonflement. Les performances en assemblage membrane électrodes ont été évaluées pour mettre en évidence l’avantage de la présence de ces nanoargiles dans les membranes composites en ce qui concerne l’humidité relative du gaz d’alimentation, la température de fonctionnement de la cellule et la perméation à l’hydrogène. Des résumés détaillés comprenant les principaux résultats ont été fournis au début de chaque chapitre.

International audience

International audience

International audience; An intelligent integration and a coordinated use of distributed generation and small-scale energy storage devices (DSO and client owned) combined with the flexibility provided by end-customers can significantly contribute to improve the robustness, resilience and flexibility of the distribution grid (MV/LV), a common goal for Distribution System Operators. SENSIBLE 1 project aims for energy storage sustainability and three demonstrators were deployed (Portugal, England, Germany) in order to prove the capabilities of distributed small-scale Energy Storage Systems (ESS). In Portugal, 5 Use Cases (UC) were tested to demonstrate storage for grid operation and end-customer applications. From high-level tools to low-level control systems (grid and residential automation), a complex ICT architecture was developed in order to enable the conditions to test all the Évora's functionalities. This paper presents the final results of the real environment challenging tests focusing on the technical, economical and societal conclusions Évora (Portugal) demonstrator.

International audience; We consider the problem of controlling heterogeneous controllable resources of a distribution network with the objective of achieving a certain power flow at the grid connection point while respecting local grid constraints. The problem is formulated as a model predictive control (MPC), where a linearized grid model, to retain convexity, based on sensitivity coefficients (SCs) is used to model the grid constraints. We consider and compare the modelling performance of three different update policies for the SCs: when they are updated once per day considering static injections, updated once per day considering point prediction of the nodal injections, and recursively estimated using on-line measurements. Simulations are performed considering the CIGRÉCIGR´CIGRÉ low voltage benchmark network. Performance is evaluated in terms of convergence speed, tracking error, and constraints modeling errors. Further, we perform a sensitivity analysis on the dominant model w.r.t. the length of the predictive horizon and number of controllable units.

International audience; Decreasing the size of metallic materials to nanometre-size dimensions has always proven beneficial to increase their mass activity for electrocatalytic reactions in proton-exchange membrane fuel cells (PEMFC) and proton-exchange membrane water electrolyzers (PEMWE). However, such strategy requires adequate supports which are (i) electron-conducting, (ii) corrosion-resistant, and (iii) exhibiting opened and interconnected architecture compatible with facile gas-transport and ionomer insertion. Due to a standard potential close to 0.2 V vs. the normal hydrogen electrode, carbon corrosion of classical carbon supports slowly proceeds at a PEMFC cathode, the kinetics of this reaction being even faster at the anode of a PEMWE; this calls for alternative support materials. It is possible to meet the first and second requirements by doping tin oxide (SnO 2 , TO), a metal oxide that is stable at pH close to 0, and 0 < E < 1.5 V vs. RHE and T = 80°C, with Sb [1-6] or Nb [2]. The third requirement can also be met by structuring the metal oxide in an aerogel form. However, benefits and limitations of this strategy under simulated or real-life operating conditions remain poorly explored and will be the focus of this presentation. Here, we synthesized Pt or Ir nanoparticles and supported them onto antimony-doped (ATO) or niobium-doped (NTO) tin oxide aerogels featuring tailored porosity and tested their initial and long-term performance in simulated PEMFC or PEMWE operating conditions. By combining chemical, physical and electrochemical techniques, we show that Sb and Sn atoms are dissolved from ATO and poison the Pt catalytic sites in a PEMFC cathode. This process is accelerated during potential excursions at low electrode potential, e.g. during electrochemical characterizations or kinetic measurements. Sb dissolution also takes place in the potential range where a PEMWE anode operates. This leads to a Sb-poor surface covering a core featuring a Sb content close to the nominal on the long-term and restricts the capacity of the metal nanoparticles to exchange electrons. Despite such observations, better long-term stability under accelerated stress tests was evidenced compared to conventional carbon based supports, both in simulated PEMFC or PEMWE operating conditions.

This thesis is devoted to the short-term forecasting of electricity demand of smart homes and distribution grids. The household demand data provided by smart meters is analyzed to characterize the electricity demand at the local scale and compared to this at the regional scale, so as to examine the aggregation effect. This thorough analysis enables the designing of models that forecast the future demand. The models make use of advanced statistical tools and machine-learning techniques. The inputs are selected with special care for their relevancy to the household demand. To be deployed in an operational environment, the models must be replicable: low to no maintenance, adaptability to various situations, and robustness to the lack of data. Several demand forecasting products are developed and compared to actual datasets: probabilistic forecasts at different temporal and spatial resolutions, and daily demand scenarios. Finally, the habits related to a domestic appliance, namely the charging of an electric vehicle battery, are modeled in order to generate forecasting scenarios of the appliance demand.; Cette thèse s’intéresse à la prévision à court terme de la demande électrique d’une maison intelligente et des réseaux de distribution. Les données mesurées par les compteurs intelligents permettent de caractériser la demande électrique à l’échelle d’une maison et de la comparer à la demande régionale, pour étudier notamment l’effet de foisonnement. Cette analyse permet de développer des modèles de prévision de cette demande. Ces modèles sont de nature statistique et font usage de méthodes d’apprentissage automatique. Un soin particulier est porté à la sélection de variables d’entrée pertinentes. Afin d’être déployés dans un environnement opérationnel, les modèles doivent faire preuve de réplicabilité : fonctionnement autonome, aptitude à s’adapter à de multiple situations, et robustesse face aux données erronées. Plusieurs produits de prévision sont développés et évalués avec plusieurs jeux de données : des prévisions probabilistes à différentes résolutions, et des scénarios journaliers de la demande. Enfin, les habitudes relatives à un usage électrique particulier, à savoir le chargement d’une batterie de véhicule électrique, sont modélisées pour produire des scénarios prédictifs de la demande de cet usage spécifique.

International audience; This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind-PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion and to the contribution of each source in the aggregation.

International audience; Non-thermal plasma is an emerging alternative for removing VOC from polluted air streams. This technique has been studied in laboratory for more than twenty years and experimental data is abundant. However, mostly qualitative information has been obtained from that data and no model has been developed for predicting the treatment performance from a given set of parameters. In this paper, we establish such a model, based on experimental data extracted from 69 scientific publications. This model, obtained through a linear regression, uses both quantitative and qualitative variables to predict the energy yield of the treatment. In 80% of the data points, the measured energy yield lies between 0.6 and 1.75 times the predicted value. We also used the model to evaluate quantitatively the impact of several parameters of the treatment, such as the initial concentration, the presence of a catalyst or the reactor type. Being so, the model presented here is an invaluable tool for both scientists and engineers interested in the treatment of VOC by non-thermal plasma.

International audience

International audience; La pile à combustible à membrane échangeuse de protons (PEMFC) est un dispositif de conversion d'énergie propre déjà utilisé pour des applications portables ou de transport. Néanmoins, certains inconvénients tels liés à la durabilité et au coût doivent être surmontés avant un large déploiement de cette technologie. La réduction du prix peut être obtenue par la réduction de la quantité de platine utilisée comme catalyseur dans les couches catalytiques et particulièrement à la cathode. Différentes stratégies ont été mises en œuvre pour limiter cette quantité de platine tout en maintenant l'activité massique. Parmi celles-ci, et pour l’éliminer totalement, des recherches ont été menées sur les catalyseurs à base de métaux non précieux tels que les nitrures, carbures, chalcogénures de métaux de transition. Une dernière famille concerne des carbones dopés avec de l’azote (du bore, ou du souffre) associés avec des métaux de transitions (Fe, Co, Mn)[1]. Par exemple, des recherches ont été menées sur des catalyseurs à base d’azote et de métal de transition déposés à la surface d’un carbone suivi par divers traitements thermiques. L’activité de ces catalyseurs a été montrée et dépend de la texture (micro et meso pores) et du type de liaisons entre l’azote et le carbone et l’azote et le métal. Néanmoins de nombreux problèmes de diffusion sont observés particulièrement en assemblage membrane électrodes. Dans ce contexte, notre étude porte sur la réalisation d’une nouvelle classe de catalyseurs nanotexturés, tridimensionnels à base de carbone, d’azote et de fer. Des aérogels de carbone ont été synthétisés à partir de résorcinol, de formaldéhyde et de mélamine en présence de sel de fer. Ces matériaux ont été caractérisés physico-chimiquement (MEB, sorption d’azote, EDX, XPS, DRX) et électrochimiquement en électrode tournante (RDE). En modifiant les conditions de synthèse nous avons obtenu une large gamme de matériaux ayant des propriétés variées et ainsi nous avons relié les caractéristiques texturales et chimiques aux performances électrochimiques.

This thesis presents an optimization framework under uncertainty for the case in which an aggregator manages residential storage devices and renewable energy as sources of flexibility, participating directly in the day-ahead energy market and offering services to minimize operational costs. Residential flexibility assets are composed by batteries, electric water heaters and PV panels, which are optimally managed and controlled by an aggregator. The optimization model also considers battery’s cycling aging cost which allows capturing the non-linear relation between depth of discharge and total life cycling. The following sources of uncertainty are considered: electrical and thermal demand, PV production and energy prices. These uncertainties are included in the mathematical model by means of robust optimization theory and a methodology based on Pareto-optimality is proposed to detect the solutions with the best trade-off between cost and risk. In addition, this thesis presents a local flexibility management strategy, which is based on two products: 1) flexibility bids into a local market; and 2) local constraint support for the Distribution System Operator (DSO) in the form of maximum allowed net power and net ramping rate. An adjustable robust optimization model is proposed for coordinated management of resources and allows to demonstrate that the strategic bidding framework is robust enough to enable coordinated participation in three different marketplaces: energy, local flexibility and bilateral trading with the DSO.; Cette thèse présente un cadre d'optimisation avec incertitude dans le cas où un agrégateur gère des dispositifs de stockage résidentiels et des énergies renouvelables comme sources de flexibilité, participant directement au marché journalier de l'énergie et proposant des services visant à minimiser les coûts opérationnels. Les actifs de flexibilité résidentiels sont composés de batteries, de chauffe-eau électriques et de panneaux photovoltaïques, gérés et contrôlés de manière optimale par l'agrégateur. Le modèle d’optimisation prend également en compte le coût de vieillissement de la batterie, qui permet de capturer la relation non linéaire entre la profondeur de décharge et le cycle de vie total. Les sources d’incertitude sont: la demande électrique et thermique, la production photovoltaïque et le prix de l’énergie. Ces incertitudes sont incluses dans le modèle mathématique au moyen d'une optimisation robuste et une méthodologie est proposée pour détecter les solutions offrant le meilleur compromis entre coût et risque. De plus, cette thèse présente une stratégie de gestion de la flexibilité locale basée sur deux produits: 1) les offres sur un marché local; et 2) prise en charge de contraintes locales pour le gestionnaire du réseau de distribution (GRD) sous la forme de la puissance nette et de la rampe nette. Un modèle d'optimisation robuste ajustable est proposé pour la gestion coordonnée des ressources et permet de démontrer que le cadre d'appel d'offres stratégique est suffisamment robuste rendant possible une participation coordonnée sur trois marchés différents: l'énergie, la flexibilité locale et les échanges bilatéraux (GRD).